1416: Changes in Prevalence, Severity of Illness, and Mortality of Sepsis Admissions Between 2008 and 2015

Badawi, Omar; Liu, Xinggang; Hassan, Erkan; Swami, Sunil

doi:10.1097/01.ccm.0000529418.22941.d5

Cited by 1 publication

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“…The diagnoses with high risk of misclassification based on these responses were collapsed with other physiologically similar diagnoses based on the body system and operative status. For example, although many cases are distinct, the difference between pulmonary sepsis and pneumonia may not always be clear, and criteria have evolved (11, 14). We opted to sacrifice an increase in accuracy to minimize systemic bias in the documentation by collapsing these into a single diagnosis group, ensuring stability as the pattern of diagnosis shifts across ICUs and over time.…”

Section: Methodsmentioning

confidence: 99%

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Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias

Liu¹,

Armaignac²,

Becker³

et al. 2022

Critical Care Medicine

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Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias OBJECTIVES: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. DESIGN:Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts. Generalized additive models (GAM) with features designed to mitigate biases introduced from documentation of admission diagnosis, Glasgow Coma Scale (GCS), and extreme vital signs were developed using clinical features representing the first 24 hours of ICU admission. SETTING: eICU Research Institute database derived from ICUs participating in the Philips eICU telecritical care program.

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Section: Methodsmentioning

confidence: 99%

“…Glasgow Coma Scale scores(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) with unable to score due to medication, NA; last entry at 24 hr of ICU admission a Required data for model development.…”

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confidence: 99%